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"Construction cost"
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Comparison of the Embodied Carbon Emissions and Direct Construction Costs for Modular and Conventional Residential Buildings in South Korea
Modular construction is an innovative new construction method that minimizes waste and improves efficiency within the construction industry. However, practitioners are hampered by the lack of environmental and economic sustainability analysis methods in this area. This study analyzes the embodied carbon emissions and direct construction costs incurred during the production phase of a modular residential building and provides comparison to an equivalent conventional residential building. Major drawings and design details for a modular residential building in South Korea were obtained, and the quantity take-off data for the major construction materials were analyzed for a modular construction method and a conventional construction method using a reinforced concrete structure under the same conditions. Focusing on major construction materials during the production phase, the embodied carbon emissions assessment revealed that adopting a modular construction approach reduced the environmental impact by approximately 36%, as compared to the conventional reinforced concrete method. However, in terms of the direct construction cost, the modular construction was approximately 8% more expensive than the conventional reinforced concrete construction method.
Journal Article
BIM and Digital Tools for State-of-the-Art Construction Cost Management
Cost overrun has remained a key risk of construction projects that can be prevented by utilizing new technologies. This paper aims to identify the gap in the literature, which can potentially be addressed by using digital tools and technologies, by reviewing the current and state of the art practices. The paper presents the results of a systematic and critical content reviews on cost overruns, to address the question of what factors are affecting the cost overrun. This paper also reviews how building information modeling (BIM) in conjunction with other tools, such as the common tools in the Asia and Asia Pacific regions, are used for cost estimation and monitoring. The paper presents the results of the content review, including their contributions and limitations, which are also used to set key directions for future investigations. A total of 176 papers was identified to develop the construction cost management (CCM) dataset. The method was a mix of systematic reviews, including co-authorship network analyses, co-occurrence analytical map development covering 5671 keywords, and content analysis including theme identification and a critical review of selected papers. The paper critically reviewed 63 selected papers from CCM, which are divided into four clusters based on their scopes: BIM adoption for cost estimation and quantity surveying; BIM implementation for a bill of quantity, risk paths, and cost overruns; cost control and management; and, finally, BIM, virtual design, and value management. A trend analysis using a set of 16 themes (e.g., 3D model, BIM, Decision, Energy, and Life Cycle) for all the papers over the past ten years was developed. The content of each cluster of papers was reviewed based on the frequency of the selected themes in each cluster. The content of each cluster of papers was also reviewed critically and gaps were identified, so a set of directions for future investigations are presented.
Journal Article
Machine learning regression for estimating the cost range of building projects
Purpose
Several factors influence the costs of buildings. Thus, identifying the cost significant factors can assist to improve the accuracy of project cost forecasts during the planning phase. This paper aims to identify the cost significant parameters and explore the potential for improving the accuracy of cost forecasts for buildings using machine learning techniques and large data sets.
Design/methodology/approach
The Australian State of Victoria Building Authority data sets, which comprise various parameters such as cost of the buildings, materials used, gross floor areas (GFA) and type of buildings, have been used. Five different machine learning regression models, such as decision tree, linear regression, random forest, gradient boosting and k-nearest neighbor were used.
Findings
The findings of the study showed that among the chosen models, linear regression provided the worst outcome (r2 = 0.38) while decision tree (r2 = 0.66) and gradient boosting (r2 = 0.62) provided the best outcome. Among the analyzed features, the class of buildings explained about 34% of the variations, followed by GFA and walls, which both accounted for 26% of the variations.
Originality/value
The output of this research can provide important information regarding the factors that have major impacts on the costs of buildings in the Australian construction industry. The study revealed that the cost of buildings is highly influenced by their classes.
Journal Article
Sustainable development goals under threat: the impact of inflation on construction projects
PurposeDespite advancements in construction digitalisation and alternative building technologies, cost overrun is still a challenge in the construction industry. The inflation rate is increasing, especially in developing countries, and is critical in cost overrun matters. It can deviate construction built-up rate components. This may thwart improving construction-related Sustainable Development Goals (SDGs). Studies concerning the impact of the inflation rate on construction-related SDGs are scarce in developing countries, including Nigeria. The study investigated the impact of inflation on Nigeria’s construction projects and their outcome on SDGs and suggested possible ways to improve achievement of construction-related SDGs and their targets.Design/methodology/approachThe researchers employed a qualitative research design. This is because of the study’s unexplored dimension. The researchers engaged 35 participants across major cities in Nigeria via semi-structured virtual and face-to-face interviews. The research utilised a thematic method for collated data and accomplished saturation.FindingsFindings reveal that the impact of inflation on construction projects, if not checked, could hinder achieving construction-related SDGs in Nigeria. This is because of the past three years of hyperinflation that cut across major construction components. It shows that the upward inflation rate threatens achieving construction-related SDGs and proffered measures to mitigate inflation and, by extension, enhance achieving construction-related SDGs. This includes a downward review of the Monetary Policy Rate, control of exchange rate volatility and addressing insecurity to restore FDIs and FPIs confidence.Originality/valueBesides suggesting possible solutions to mitigate hyperinflation on construction components to improve achieving construction-related SDGs, findings will stipulate government policymakers put measures in place through favourable fiscal and monetary policy implementation and encourage moving from a consumption to a production nation.
Journal Article
Uncertainty aware and explainable construction cost prediction using a hybrid probabilistic learning model
This paper presents a unified probabilistic framework for construction cost forecasting, NGBoost-ETR (Natural Gradient Boosting with Extra Trees base learners) that delivers predictive accuracy, calibrated uncertainty, and SHAP-based interpretability. Rather than positioning novelty in algorithmic integration alone, the contribution lies in a systems-level design that jointly addresses three critical gaps in cost modeling: reliable interval calibration, model interpretability, and robustness across complex feature interactions in domain such as sustainable building. Trained on a real-world RSMeans dataset of 4477 samples, NGBoost-ETR achieves superior predictive performance (R
2
= 0.9866, RMSE = 0.4986, MSE = 0.2486, MAE = 0.2300, and MAPE = 1.4314%) compared to 10 baseline regressors and 9 NGBoost-based hybrids. Beyond point prediction, the model also demonstrates robust probabilistic calibration, validated through a comprehensive suite of six quantitative metrics. Specifically, evaluation based on Prediction Interval Coverage Probability (PICP), Prediction Interval Normalized Average Width (PINAW), Mean Prediction Interval Width (MPIW), Coverage Width-based Criterion (CWC), Negative Log-Likelihood (NLL), and Continuous Ranked Probability Score (CRPS). In head-to-head ablations, NGBoost-ETR attains the best interval efficiency (lowest PINAW, MPIW), overall calibration (lowest CWC), and distributional accuracy (lowest CRPS), with competitive NLL and acceptable coverage—outperforming variants that inflate coverage via impractically wide intervals. Crucially, not all hybrids are beneficial (e.g., some NGBoost pairings underperform their base learners), emphasizing that the ETR pairing is a validated choice rather than a generic integration. This work introduces a process innovation by embedding reliable uncertainty estimation into tree-based models without sacrificing performance, supporting greater resource efficiency in cost planning and estimation. The resulting framework not only supports data-driven budgeting and tendering but also promotes transparent institutions and risk-aware decision-making in construction management.
Journal Article
DSCostPred: a double-stacking model for construction cost prediction
The prediction of construction project cost plays a core role in engineering construction projects. However, the current prediction involves a multi-dimensional and dynamically variable system, and each major category can be further subdivided into many specific factors. Meanwhile, variables’ relationships present a complex network of nonlinearity and interaction, which seriously affected the prediction accuracy. To solve this problem, we proposed a dual-stacking construction cost prediction method based on variable stacking and model stacking (DSCostPred). This method emphasizes that classifying variables and applying different algorithms respectively can avoid the impact of variables’ functional differences. First, the variables are pre-classified to avoid mutual interference among them. Then, to learn the attribute and function positioning, as well as the complex interaction among them, different types of models are utilized to learn the variables. In algorithm design, to achieve the organic combination of multiple attributes and multiple models, a variable stacking is introduced into stacking ensemble learning to form collaborative predictions with model stacking. This method was compared with the classical method on real data, and the results show the superior performance. In addition, the ablation experiments and SHAP analysis also demonstrated the feasibility of the double-stacking idea we proposed.
Journal Article
Construction cost prediction model for agricultural water conservancy engineering based on BIM and neural network
Due to the complex construction conditions, long work cycles, and high uncertainty inherent in agricultural water conservancy projects, accurate construction cost prediction is crucial for investment decisions. This study presents an innovative cost prediction model for these projects, integrating BIM with neural networks. Firstly, BIM technology is utilized to digitize and visualize engineering-related information. Subsequently, a prediction model based on SSA optimized PGNN is constructed. The digital data obtained from BIM is subsequently integrated with the prediction model to estimate the construction costs of agricultural water conservancy projects. In this study, actual engineering projects are selected as case studies, utilizing material price data from January 2016 to February 2021 in Liaoning Province, along with real project data for modeling purposes. The results indicate that the maximum relative error between the predicted and actual values of the combined model is only 2.99%. Furthermore, the RMSE and
R
2
of the simulated prediction results are 0.1358 and 0.9819, respectively. The proposed model demonstrates higher prediction accuracy and efficiency. Compared with the PGNN model, the RMSE is reduced by 33%, and
R
2
is increased by 6%. These findings suggest that the BIM-SSA-PGNN prediction model provides more accurate and efficient construction cost predictions for agricultural water conservancy projects, promoting technological integration and innovation while optimizing construction project costs. This study provides a scientific basis for management to promote the transformation of the industry towards digital and intelligent sustainable development.
Journal Article